R语言 缺失值NaN

bogh5gae  于 2023-02-20  发布在  其他
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请帮助我，有一个错误，当我试图使输出与测试数据10%（训练数据90）与1000倍的模拟。

  1. #pemanggilan paket yang digunakan
  2. library(caret)
  3. library(KernelKnn)
  5. #perhitungan akurasi prediksi dengan 100 kali simulasi
  6. sim = function(B, p1, k1)
  7. {
  8.   hasileu = matrix(0, B, 4)
  9.   for (i in 1:B)
  10.   {
  11.     #memanggil data boston
  12.     boston = MASS::Boston
  14.     #membagi data train dan data test
  15.     indexes = createDataPartition(boston$medv, p = p1, list = F)
  16.     train = boston[indexes, ]
  17.     test = boston[-indexes, ]
  18.     
  19.     train_x = train[, -14]
  20.     train_x = scale(train_x)[,]
  21.     train_y = train[,14]
  22.     
  23.     test_x = test[, -14]
  24.     test_x = scale(test[,-14])[,]
  25.     test_y = test[,14]
  26.     
  27.     #model prediksi regresi KNN
  28.     pred1 = KernelKnn(train_x, TEST_data = test_x, train_y, k = k1, method = 'euclidean', weights_function = NULL, regression = T)
  30.     #perhitungan akurasi
  31.     mse = mean((test_y - pred1)^2)
  32.     mae = mean(abs(test_y - pred1))
  33.     rmse = sqrt(mse)
  34.     mape = mean(abs((test_y - pred1)/test_y))
  35.     r2 = cor(test_y, pred1)^2
  36.     hasileu[i,1] = rmse
  37.     hasileu[i,2] = mape
  38.     hasileu[i,3] = mae
  39.     hasileu[i,4] = r2
  40.   }
  41.   return(apply(hasileu, 2, mean))
  42. }
  44. #penentuan rentang nilai K
  45. hitung.variasi.k = function(B,p,K)
  46. {
  47.   has11 = matrix(0,K,4)
  48.   for (i in 1:K)
  49.   {
  50.     has11[i,] = sim(B,p,i)
  51.   }
  52.   has11
  53. }
  55. #hasil akurasi berbagai proporsi data uji
  56. K = 10
  57. has11 = hitung.variasi.k(1000, 0.9,K)
  59. Error in KernelKnn(train_x, TEST_data = test_x, train_y, k = k1, method = "euclidean", : 
  60. the TEST_data includes missing values
r

1条答案

按热度按时间
blmhpbnm

blmhpbnm1#

下面的代码使用tryCatch捕获“error”并继续执行到最后。

  1. library(caret) 
  2. library(KernelKnn)
  4. #perhitungan akurasi prediksi dengan 100 kali simulasi
  5. sim = function(B, p1, k1)
  6. {
  7.   hasileu = matrix(0, B, 4)
  8.   for (i in 1:B)
  9.   {
  10.     #memanggil data boston
  11.     boston = MASS::Boston
  12.     
  13.     #membagi data train dan data test
  14.     indexes = createDataPartition(boston$medv, p = p1, list = F)
  15.     train = boston[indexes, ]
  16.     test = boston[-indexes, ]
  17.     
  18.     train_x = train[, -14]
  19.     train_x = scale(train_x)[,]
  20.     train_y = train[,14]
  21.     
  22.     test_x = test[, -14]
  23.     test_x = scale(test[,-14])[,]
  24.     test_y = test[,14]
  25.     
  26.     errorFlag=0
  27.     #model prediksi regresi KNN
  28.     pred1 =tryCatch(
  29.      KernelKnn(train_x, TEST_data = test_x, train_y, k = k1, method = 'euclidean', weights_function = NULL, regression = T)
  30.     , error=function(e) 
  31.       { print(e); print(paste("error at loop", i));errorFlag<<-1;}
  32.     )
  34.     if (errorFlag==0)
  35.     {
  36.       #perhitungan akurasi
  37.       mse = mean((test_y - pred1)^2)
  38.       mae = mean(abs(test_y - pred1))
  39.       rmse = sqrt(mse)
  40.       mape = mean(abs((test_y - pred1)/test_y))
  41.       r2 = cor(test_y, pred1)^2
  42.       hasileu[i,1] = rmse
  43.       hasileu[i,2] = mape
  44.       hasileu[i,3] = mae
  45.       hasileu[i,4] = r2
  46.     }
  47.   }
  48.   return(apply(hasileu, 2, mean))
  49. }
  51. #penentuan rentang nilai K
  52. hitung.variasi.k = function(B,p,K)
  53. {
  54.   has11 = matrix(0,K,4)
  55.   for (i in 1:K)
  56.   {
  57.     has11[i,] = sim(B,p1=p,k1=i)
  58.   }
  59.   return(has11)
  60. }
  62. #hasil akurasi berbagai proporsi data uji
  63. K = 10
  64. has11 = hitung.variasi.k(B=1000, p=0.9,K=10)
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